SENTIMENT ANALYTICS

Sentiment analytics, also know as opinion mining, refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective sentiment information from source materials. Utilizing these tools to uncover the emotional intonation that the articles written about your stock can disclose. This is often valuable and often obscured investment information.

Sentiment analytics, also know as opinion mining, refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective sentiment information from source materials. This can be very valuable when you have read an article and come to the conclusion that the impact will be positive or negative. After you analyze the article utilizing word sentiment analytics, you might well discover that the word analytics is not as positive or negative as you first thought. These analytical scores can also dramatically change after comments have been posted in response to an article

• The software that will drive the backend of this analytical engine is • • still being written and this feature of the SDG website will be available in a few weeks. •

• • • • • • • Enter URL in top box on right of graph and hit the enter key on your keyboard to see the results. • • • • • • • • • • • • • • Copy and paste the text you want to examine into the larger box on the right and hit enter to analyize. • • • • • •

Word Analytics is a proven method of determing the sentiment by analyzing the intonation of the words in any published documents. Crowd sentiment can be determined by aiming this tool at multiple documents. This textual information can be about a company, sector or any topic. Analyzing the textual content of an article that is primary about our target stock or includes our stock in the topic it discusses provides us with very valuable information. This textural color provides us with another strong indicator of the potential sentiment that will drive future demand. We are primarily looking for positive and negative interpretations, from which we can infer the documents overall sentiment. This process enables us to evulate large amount of information and maintain current sentiment evualations of individual equities.

The SDG Word Analytics tool also provides you with weak and strong modal words, legalistic words and words that are ambiguous or uncertain. The bar graph above will provide you with a broad picture of the textual intonation of the article or website your are examining, enabling you to determine what direction the stock will be propelled by the sentiment expressed in this article or document. The two slides below will help you to understand the somewhat ambiguous terms of weak and strong modal. The source of these slides will further expand on that understanding. Click on slides to enlarge.

The SDG word analytics tool provides us with a percentage/ratio of the positive versuses negative words utilized in our captured text for any given stock. When attempting to interperate any equities move, one direction or another, the more indicators you have and the more accurate they are, the more likely you are to be able to anticipate future directional moves correctly, allowing you to stay in profitable trends and exit them in a timely manor. More information about this process can be found here.

SDG has another insightful sentiment detection tool included in the Tradingview package. The relative position of the close of the day is an important indicator on the sentiment of the traders of the equity for that day. More information on CS100 can be found here.

A critical componment for this process to be sucesfull when analyzing financial documents is utilizing the correct buckets of words for comparison to the target document. These financial textual elements were identified by Tim Loughran and Bill McDonald from an article they published in; THE JOURNAL OF FINANCE, VOL. LXVI, NO. 1, FEBRUARY 2011. You can find the specific word lists here on their website and their article here.